Psychometric validation of the Bengali version of the 10 item MARS(Medication Adherence Rating Scale) among bipolar disorder patients, Kolkata West Bengal.
Bibliographic record
Abstract
Background: Bipolar affective is a chronic mental illness, where recurrence is very common .Bipolar patients have low or partial adherence rate to treatment. The MARS is a 10 item self report measure of medication compliance. Medication adherence is a vital predictor of illness course and outcome in patients with bipolar disorder. Aim: 1. To validate the English to Bengali translation of MARS. To establish the reliability of the MARS scale. Material and Method: The original scale was translated to Bengali version by the following process: Establishment of a bilingual group of experts.2. Examination of the conceptual structure of the instrument under study by the experts. 3. Translation 4. Examination of the translation by the experts. 5. Examination of the translation by a monolingual group. 6. Blind back translation7. Examination of the blind back translation by the experts. The Psychometric properties of MARS-B were validated among the bipolar patients (N=100). Data were collected during the period of May 2023 to July2023.by interviewing with simple random sampling and analyzed by statistical package of social science(SPSS -16) and MS Excel 2010 version software. Results: This study report shows good reliability Cronbach’s (α) 0.71 of MARS (Bengali) version. principal-components analysis extracted four factors from the MARS that together accounted for 55% of the variance. These factors were then subjected to a Varimax rotation revealing the factors of ‘Medication adherence behavior’, ‘Health status and wellbeing causes medication adherence’, ‘Perception regarding benefits of medication adherence’,and ‘Negative side effects towards psychotropic medication’ . Conclusion: This study shows that MARS Bengali version is valid, reliable and applicable in clinical practice, research, public health and primary health care in west Bengal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".